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2024 | OriginalPaper | Chapter

Machine Learning for Forecasting Depression and Anxiety in University Students

Authors : Tamal Biswas, Diptendu Bhattacharya, Dwijen Rudrapal, Srijan Roy

Published in: ICT: Applications and Social Interfaces

Publisher: Springer Nature Singapore

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Abstract

The timely identification of mental health issues enables experts to more effectively provide treatment and enhance the well-being of patients. Mental health pertains to an individual’s emotional, mental, and interpersonal state, influencing their thoughts, emotions, and behaviors. It remains crucial across all life phases, spanning childhood, adolescence, and adulthood. Historically, categorizing mental health problems among college students demanded significant effort and time from psychologists. This research engaged five machine learning methods to classify such issues swiftly. The effectiveness of these methods was evaluated based on different standards. The five methods included logistic regression, KNN classification, decision tree classification, random forest, and stacking. A comparison and implementation of these methods revealed that stacking yielded the highest accuracy, predicting 82.20.

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previous chapter Cheating Detection in Online Exam: A Comprehensive Survey
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Metadata
Title
Machine Learning for Forecasting Depression and Anxiety in University Students
Authors
Tamal Biswas
Diptendu Bhattacharya
Dwijen Rudrapal
Srijan Roy
Copyright Year
2024
Publisher
Springer Nature Singapore
Book
ICT: Applications and Social Interfaces

Print ISBN: 978-981-9702-09-1

Electronic ISBN: 978-981-9702-10-7

Copyright Year: 2024

DOI
https://doi.org/10.1007/978-981-97-0210-7_7